Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations100000
Missing cells219974
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.9 MiB
Average record size in memory774.6 B

Variable types

Categorical4
Text6
DateTime1
Numeric7

Alerts

Engagement Rate is highly overall correlated with Likes and 1 other fieldsHigh correlation
Likes is highly overall correlated with Engagement RateHigh correlation
Reach is highly overall correlated with Engagement RateHigh correlation
Campaign ID has 79868 (79.9%) missing values Missing
Sentiment has 50100 (50.1%) missing values Missing
Influencer ID has 90006 (90.0%) missing values Missing
Post ID has unique values Unique
Post Content has unique values Unique
Post Timestamp has unique values Unique

Reproduction

Analysis started2025-01-05 15:11:54.526834
Analysis finished2025-01-05 15:12:02.196814
Duration7.67 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Platform
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Twitter
25160 
LinkedIn
25126 
Facebook
24879 
Instagram
24835 

Length

Max length9
Median length8
Mean length7.99675
Min length7

Characters and Unicode

Total characters799675
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLinkedIn
2nd rowInstagram
3rd rowInstagram
4th rowTwitter
5th rowFacebook

Common Values

ValueCountFrequency (%)
Twitter 25160
25.2%
LinkedIn 25126
25.1%
Facebook 24879
24.9%
Instagram 24835
24.8%

Length

2025-01-05T20:42:02.248144image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T20:42:02.316956image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
twitter 25160
25.2%
linkedin 25126
25.1%
facebook 24879
24.9%
instagram 24835
24.8%

Most occurring characters

ValueCountFrequency (%)
e 75165
 
9.4%
t 75155
 
9.4%
n 75087
 
9.4%
a 74549
 
9.3%
i 50286
 
6.3%
k 50005
 
6.3%
r 49995
 
6.3%
I 49961
 
6.2%
o 49758
 
6.2%
w 25160
 
3.1%
Other values (9) 224554
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 799675
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 75165
 
9.4%
t 75155
 
9.4%
n 75087
 
9.4%
a 74549
 
9.3%
i 50286
 
6.3%
k 50005
 
6.3%
r 49995
 
6.3%
I 49961
 
6.2%
o 49758
 
6.2%
w 25160
 
3.1%
Other values (9) 224554
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 799675
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 75165
 
9.4%
t 75155
 
9.4%
n 75087
 
9.4%
a 74549
 
9.3%
i 50286
 
6.3%
k 50005
 
6.3%
r 49995
 
6.3%
I 49961
 
6.2%
o 49758
 
6.2%
w 25160
 
3.1%
Other values (9) 224554
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 799675
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 75165
 
9.4%
t 75155
 
9.4%
n 75087
 
9.4%
a 74549
 
9.3%
i 50286
 
6.3%
k 50005
 
6.3%
r 49995
 
6.3%
I 49961
 
6.2%
o 49758
 
6.2%
w 25160
 
3.1%
Other values (9) 224554
28.1%

Post ID
Text

Unique 

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2025-01-05T20:42:02.447009image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters3600000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100000 ?
Unique (%)100.0%

Sample

1st rowd49dadb4-fc1e-4775-88ad-d60b75cf5123
2nd row16ce29c5-2dc7-478f-9814-a86754f3ce8b
3rd row6a6cc741-72f4-4111-95a0-c5ee04a175a8
4th row0b5a3e70-c556-43cb-ad4f-a1d0003348bc
5th rowa7d3e958-e2fe-411e-a24a-b96e821fc050
ValueCountFrequency (%)
e60fa172-9bff-4c44-b984-d6d964590291 1
 
< 0.1%
724e5246-11d1-426a-9904-aa57b3f45722 1
 
< 0.1%
cee637d2-97bb-444b-a649-6e5e296bbb2b 1
 
< 0.1%
4c84bdbf-5f96-4cff-88dd-2301e01bc152 1
 
< 0.1%
b889d00b-3172-44ab-be84-72afee1a01f8 1
 
< 0.1%
725ff4a8-33b0-4698-971c-1020648f17eb 1
 
< 0.1%
d633444f-9f4a-41e9-89b6-a0207c23da1b 1
 
< 0.1%
2862509e-a3f1-4845-8735-a1b939d224cf 1
 
< 0.1%
3f4d6bd5-c29a-4a8a-a0ef-7f34b9851acb 1
 
< 0.1%
ffbccca5-680a-4a10-a08f-0369c563399d 1
 
< 0.1%
Other values (99990) 99990
> 99.9%
2025-01-05T20:42:02.655016image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 400000
 
11.1%
4 287225
 
8.0%
a 212852
 
5.9%
8 212743
 
5.9%
b 212388
 
5.9%
9 212154
 
5.9%
3 188118
 
5.2%
c 188079
 
5.2%
7 187930
 
5.2%
1 187598
 
5.2%
Other values (7) 1310913
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3600000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 400000
 
11.1%
4 287225
 
8.0%
a 212852
 
5.9%
8 212743
 
5.9%
b 212388
 
5.9%
9 212154
 
5.9%
3 188118
 
5.2%
c 188079
 
5.2%
7 187930
 
5.2%
1 187598
 
5.2%
Other values (7) 1310913
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3600000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 400000
 
11.1%
4 287225
 
8.0%
a 212852
 
5.9%
8 212743
 
5.9%
b 212388
 
5.9%
9 212154
 
5.9%
3 188118
 
5.2%
c 188079
 
5.2%
7 187930
 
5.2%
1 187598
 
5.2%
Other values (7) 1310913
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3600000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 400000
 
11.1%
4 287225
 
8.0%
a 212852
 
5.9%
8 212743
 
5.9%
b 212388
 
5.9%
9 212154
 
5.9%
3 188118
 
5.2%
c 188079
 
5.2%
7 187930
 
5.2%
1 187598
 
5.2%
Other values (7) 1310913
36.4%

Post Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
Video
33384 
Link
33338 
Image
33278 

Length

Max length5
Median length5
Mean length4.66662
Min length4

Characters and Unicode

Total characters466662
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVideo
2nd rowVideo
3rd rowVideo
4th rowImage
5th rowLink

Common Values

ValueCountFrequency (%)
Video 33384
33.4%
Link 33338
33.3%
Image 33278
33.3%

Length

2025-01-05T20:42:02.734115image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T20:42:02.797912image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
video 33384
33.4%
link 33338
33.3%
image 33278
33.3%

Most occurring characters

ValueCountFrequency (%)
i 66722
14.3%
e 66662
14.3%
V 33384
7.2%
d 33384
7.2%
o 33384
7.2%
L 33338
7.1%
n 33338
7.1%
k 33338
7.1%
I 33278
7.1%
m 33278
7.1%
Other values (2) 66556
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 466662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 66722
14.3%
e 66662
14.3%
V 33384
7.2%
d 33384
7.2%
o 33384
7.2%
L 33338
7.1%
n 33338
7.1%
k 33338
7.1%
I 33278
7.1%
m 33278
7.1%
Other values (2) 66556
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 466662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 66722
14.3%
e 66662
14.3%
V 33384
7.2%
d 33384
7.2%
o 33384
7.2%
L 33338
7.1%
n 33338
7.1%
k 33338
7.1%
I 33278
7.1%
m 33278
7.1%
Other values (2) 66556
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 466662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 66722
14.3%
e 66662
14.3%
V 33384
7.2%
d 33384
7.2%
o 33384
7.2%
L 33338
7.1%
n 33338
7.1%
k 33338
7.1%
I 33278
7.1%
m 33278
7.1%
Other values (2) 66556
14.3%

Post Content
Text

Unique 

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2025-01-05T20:42:02.989208image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length75
Median length58
Mean length36.02079
Min length9

Characters and Unicode

Total characters3602079
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100000 ?
Unique (%)100.0%

Sample

1st rowWant that according same wish.
2nd rowIncrease fast partner south.
3rd rowLawyer behavior born window couple sister.
4th rowStreet great decade must.
5th rowAt clear meeting with reason ground continue contain.
ValueCountFrequency (%)
if 667
 
0.1%
serious 647
 
0.1%
reach 646
 
0.1%
sea 645
 
0.1%
career 640
 
0.1%
green 640
 
0.1%
draw 639
 
0.1%
capital 637
 
0.1%
pay 635
 
0.1%
hear 634
 
0.1%
Other values (961) 544436
98.8%
2025-01-05T20:42:03.351812image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
450866
12.5%
e 403213
 
11.2%
t 236650
 
6.6%
r 232425
 
6.5%
a 227379
 
6.3%
o 213950
 
5.9%
i 208340
 
5.8%
n 196694
 
5.5%
s 160476
 
4.5%
l 148173
 
4.1%
Other values (42) 1123913
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3602079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
450866
12.5%
e 403213
 
11.2%
t 236650
 
6.6%
r 232425
 
6.5%
a 227379
 
6.3%
o 213950
 
5.9%
i 208340
 
5.8%
n 196694
 
5.5%
s 160476
 
4.5%
l 148173
 
4.1%
Other values (42) 1123913
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3602079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
450866
12.5%
e 403213
 
11.2%
t 236650
 
6.6%
r 232425
 
6.5%
a 227379
 
6.3%
o 213950
 
5.9%
i 208340
 
5.8%
n 196694
 
5.5%
s 160476
 
4.5%
l 148173
 
4.1%
Other values (42) 1123913
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3602079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
450866
12.5%
e 403213
 
11.2%
t 236650
 
6.6%
r 232425
 
6.5%
a 227379
 
6.3%
o 213950
 
5.9%
i 208340
 
5.8%
n 196694
 
5.5%
s 160476
 
4.5%
l 148173
 
4.1%
Other values (42) 1123913
31.2%

Post Timestamp
Date

Unique 

Distinct100000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
Minimum2021-03-08 19:13:37.452507
Maximum2024-03-08 11:33:25.534234
Invalid dates0
Invalid dates (%)0.0%
2025-01-05T20:42:03.426737image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:03.507128image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Likes
Real number (ℝ)

High correlation 

Distinct1001
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.39624
Minimum0
Maximum1000
Zeros109
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-01-05T20:42:03.585243image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1249
median500
Q3750
95-th percentile951
Maximum1000
Range1000
Interquartile range (IQR)501

Descriptive statistics

Standard deviation289.09779
Coefficient of variation (CV)0.57889461
Kurtosis-1.2004555
Mean499.39624
Median Absolute Deviation (MAD)250
Skewness0.0048045865
Sum49939624
Variance83577.533
MonotonicityNot monotonic
2025-01-05T20:42:03.657419image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
574 137
 
0.1%
256 132
 
0.1%
546 127
 
0.1%
407 126
 
0.1%
708 124
 
0.1%
547 124
 
0.1%
129 124
 
0.1%
980 124
 
0.1%
807 123
 
0.1%
257 122
 
0.1%
Other values (991) 98737
98.7%
ValueCountFrequency (%)
0 109
0.1%
1 101
0.1%
2 106
0.1%
3 102
0.1%
4 89
0.1%
5 94
0.1%
6 106
0.1%
7 76
0.1%
8 116
0.1%
9 95
0.1%
ValueCountFrequency (%)
1000 106
0.1%
999 108
0.1%
998 107
0.1%
997 96
0.1%
996 113
0.1%
995 95
0.1%
994 104
0.1%
993 112
0.1%
992 92
0.1%
991 112
0.1%

Comments
Real number (ℝ)

Distinct501
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.6992
Minimum0
Maximum500
Zeros172
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-01-05T20:42:03.722754image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1124
median250
Q3375
95-th percentile475
Maximum500
Range500
Interquartile range (IQR)251

Descriptive statistics

Standard deviation144.6112
Coefficient of variation (CV)0.57914162
Kurtosis-1.2025845
Mean249.6992
Median Absolute Deviation (MAD)126
Skewness-0.0026294217
Sum24969920
Variance20912.399
MonotonicityNot monotonic
2025-01-05T20:42:03.803242image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
318 240
 
0.2%
401 235
 
0.2%
430 233
 
0.2%
429 233
 
0.2%
386 232
 
0.2%
400 231
 
0.2%
60 231
 
0.2%
18 229
 
0.2%
12 229
 
0.2%
258 228
 
0.2%
Other values (491) 97679
97.7%
ValueCountFrequency (%)
0 172
0.2%
1 177
0.2%
2 223
0.2%
3 198
0.2%
4 215
0.2%
5 215
0.2%
6 176
0.2%
7 225
0.2%
8 196
0.2%
9 221
0.2%
ValueCountFrequency (%)
500 190
0.2%
499 214
0.2%
498 166
0.2%
497 199
0.2%
496 180
0.2%
495 186
0.2%
494 178
0.2%
493 188
0.2%
492 189
0.2%
491 210
0.2%

Shares
Real number (ℝ)

Distinct201
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.11851
Minimum0
Maximum200
Zeros515
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-01-05T20:42:03.867081image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q150
median100
Q3150
95-th percentile190
Maximum200
Range200
Interquartile range (IQR)100

Descriptive statistics

Standard deviation57.924815
Coefficient of variation (CV)0.57856249
Kurtosis-1.1950335
Mean100.11851
Median Absolute Deviation (MAD)50
Skewness-0.0013119681
Sum10011851
Variance3355.2841
MonotonicityNot monotonic
2025-01-05T20:42:03.950392image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 549
 
0.5%
14 549
 
0.5%
138 545
 
0.5%
88 543
 
0.5%
176 542
 
0.5%
164 541
 
0.5%
58 541
 
0.5%
196 538
 
0.5%
177 537
 
0.5%
99 537
 
0.5%
Other values (191) 94578
94.6%
ValueCountFrequency (%)
0 515
0.5%
1 492
0.5%
2 469
0.5%
3 495
0.5%
4 513
0.5%
5 442
0.4%
6 510
0.5%
7 487
0.5%
8 488
0.5%
9 506
0.5%
ValueCountFrequency (%)
200 479
0.5%
199 484
0.5%
198 463
0.5%
197 496
0.5%
196 538
0.5%
195 525
0.5%
194 532
0.5%
193 505
0.5%
192 481
0.5%
191 444
0.4%

Impressions
Real number (ℝ)

Distinct9001
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5487.6291
Minimum1000
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-01-05T20:42:04.015756image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1446
Q13239
median5477
Q37733
95-th percentile9541
Maximum10000
Range9000
Interquartile range (IQR)4494

Descriptive statistics

Standard deviation2594.6742
Coefficient of variation (CV)0.47282245
Kurtosis-1.1947548
Mean5487.6291
Median Absolute Deviation (MAD)2247
Skewness0.0044486304
Sum5.4876291 × 108
Variance6732334.2
MonotonicityNot monotonic
2025-01-05T20:42:04.096948image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4136 25
 
< 0.1%
7152 25
 
< 0.1%
2457 24
 
< 0.1%
9673 24
 
< 0.1%
7139 24
 
< 0.1%
5168 24
 
< 0.1%
3559 24
 
< 0.1%
7811 24
 
< 0.1%
3867 23
 
< 0.1%
7896 23
 
< 0.1%
Other values (8991) 99760
99.8%
ValueCountFrequency (%)
1000 12
< 0.1%
1001 10
< 0.1%
1002 6
 
< 0.1%
1003 9
< 0.1%
1004 10
< 0.1%
1005 18
< 0.1%
1006 15
< 0.1%
1007 9
< 0.1%
1008 10
< 0.1%
1009 11
< 0.1%
ValueCountFrequency (%)
10000 8
< 0.1%
9999 13
< 0.1%
9998 8
< 0.1%
9997 8
< 0.1%
9996 7
< 0.1%
9995 10
< 0.1%
9994 11
< 0.1%
9993 9
< 0.1%
9992 11
< 0.1%
9991 9
< 0.1%

Reach
Real number (ℝ)

High correlation 

Distinct4501
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2751.52
Minimum500
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-01-05T20:42:04.167081image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile728
Q11627
median2754
Q33877.25
95-th percentile4776
Maximum5000
Range4500
Interquartile range (IQR)2250.25

Descriptive statistics

Standard deviation1299.3597
Coefficient of variation (CV)0.47223343
Kurtosis-1.200683
Mean2751.52
Median Absolute Deviation (MAD)1125
Skewness-0.00010224814
Sum2.75152 × 108
Variance1688335.6
MonotonicityNot monotonic
2025-01-05T20:42:04.221759image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4985 41
 
< 0.1%
3386 41
 
< 0.1%
4114 38
 
< 0.1%
2326 38
 
< 0.1%
1072 37
 
< 0.1%
1432 37
 
< 0.1%
4038 37
 
< 0.1%
1052 37
 
< 0.1%
2316 37
 
< 0.1%
3001 36
 
< 0.1%
Other values (4491) 99621
99.6%
ValueCountFrequency (%)
500 18
< 0.1%
501 18
< 0.1%
502 17
< 0.1%
503 13
< 0.1%
504 22
< 0.1%
505 24
< 0.1%
506 20
< 0.1%
507 29
< 0.1%
508 28
< 0.1%
509 22
< 0.1%
ValueCountFrequency (%)
5000 19
< 0.1%
4999 25
< 0.1%
4998 17
< 0.1%
4997 23
< 0.1%
4996 26
< 0.1%
4995 21
< 0.1%
4994 23
< 0.1%
4993 22
< 0.1%
4992 15
< 0.1%
4991 24
< 0.1%

Engagement Rate
Real number (ℝ)

High correlation 

Distinct15106
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.411362
Minimum0.49
Maximum312.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-01-05T20:42:04.303416image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile9.95
Q120.03
median30.77
Q352.3725
95-th percentile124.061
Maximum312.55
Range312.06
Interquartile range (IQR)32.3425

Descriptive statistics

Standard deviation37.746432
Coefficient of variation (CV)0.86950582
Kurtosis6.3120022
Mean43.411362
Median Absolute Deviation (MAD)13.51
Skewness2.2645663
Sum4341136.2
Variance1424.7931
MonotonicityNot monotonic
2025-01-05T20:42:04.377105image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.32 41
 
< 0.1%
25.46 40
 
< 0.1%
16.02 40
 
< 0.1%
28.33 39
 
< 0.1%
17.88 39
 
< 0.1%
25.39 38
 
< 0.1%
16.08 38
 
< 0.1%
26.3 37
 
< 0.1%
16.67 37
 
< 0.1%
20.47 37
 
< 0.1%
Other values (15096) 99614
99.6%
ValueCountFrequency (%)
0.49 1
< 0.1%
0.54 1
< 0.1%
0.72 1
< 0.1%
0.73 1
< 0.1%
0.85 1
< 0.1%
0.88 1
< 0.1%
0.94 1
< 0.1%
1.04 1
< 0.1%
1.11 1
< 0.1%
1.17 1
< 0.1%
ValueCountFrequency (%)
312.55 1
< 0.1%
309.88 1
< 0.1%
309.47 1
< 0.1%
302.75 1
< 0.1%
302.5 1
< 0.1%
298.61 1
< 0.1%
298.4 1
< 0.1%
297.02 1
< 0.1%
296.99 1
< 0.1%
296.67 1
< 0.1%

Audience Age
Real number (ℝ)

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.51474
Minimum18
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2025-01-05T20:42:04.524539image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q130
median42
Q354
95-th percentile63
Maximum65
Range47
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.867187
Coefficient of variation (CV)0.33403045
Kurtosis-1.203959
Mean41.51474
Median Absolute Deviation (MAD)12
Skewness-0.0037963226
Sum4151474
Variance192.29889
MonotonicityNot monotonic
2025-01-05T20:42:04.599131image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
18 2196
 
2.2%
33 2181
 
2.2%
38 2177
 
2.2%
45 2172
 
2.2%
58 2140
 
2.1%
55 2137
 
2.1%
50 2136
 
2.1%
21 2134
 
2.1%
60 2123
 
2.1%
30 2122
 
2.1%
Other values (38) 78482
78.5%
ValueCountFrequency (%)
18 2196
2.2%
19 1999
2.0%
20 2047
2.0%
21 2134
2.1%
22 2105
2.1%
23 2117
2.1%
24 2040
2.0%
25 2076
2.1%
26 2056
2.1%
27 2085
2.1%
ValueCountFrequency (%)
65 2062
2.1%
64 2095
2.1%
63 2060
2.1%
62 2095
2.1%
61 2059
2.1%
60 2123
2.1%
59 2083
2.1%
58 2140
2.1%
57 2092
2.1%
56 2114
2.1%

Audience Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
Male
33476 
Female
33385 
Other
33139 

Length

Max length6
Median length5
Mean length4.99909
Min length4

Characters and Unicode

Total characters499909
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowMale
3rd rowMale
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Male 33476
33.5%
Female 33385
33.4%
Other 33139
33.1%

Length

2025-01-05T20:42:04.661560image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T20:42:04.725723image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
male 33476
33.5%
female 33385
33.4%
other 33139
33.1%

Most occurring characters

ValueCountFrequency (%)
e 133385
26.7%
a 66861
13.4%
l 66861
13.4%
M 33476
 
6.7%
F 33385
 
6.7%
m 33385
 
6.7%
O 33139
 
6.6%
t 33139
 
6.6%
h 33139
 
6.6%
r 33139
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 499909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 133385
26.7%
a 66861
13.4%
l 66861
13.4%
M 33476
 
6.7%
F 33385
 
6.7%
m 33385
 
6.7%
O 33139
 
6.6%
t 33139
 
6.6%
h 33139
 
6.6%
r 33139
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 499909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 133385
26.7%
a 66861
13.4%
l 66861
13.4%
M 33476
 
6.7%
F 33385
 
6.7%
m 33385
 
6.7%
O 33139
 
6.6%
t 33139
 
6.6%
h 33139
 
6.6%
r 33139
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 499909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 133385
26.7%
a 66861
13.4%
l 66861
13.4%
M 33476
 
6.7%
F 33385
 
6.7%
m 33385
 
6.7%
O 33139
 
6.6%
t 33139
 
6.6%
h 33139
 
6.6%
r 33139
 
6.6%
Distinct243
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.5 MiB
2025-01-05T20:42:04.869174image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length51
Median length33
Mean length10.67975
Min length4

Characters and Unicode

Total characters1067975
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited Kingdom
2nd rowGreece
3rd rowUnited States Virgin Islands
4th rowZambia
5th rowAfghanistan
ValueCountFrequency (%)
islands 6654
 
4.3%
and 4536
 
2.9%
republic 2879
 
1.9%
saint 2825
 
1.8%
united 2065
 
1.3%
south 1696
 
1.1%
island 1654
 
1.1%
the 1287
 
0.8%
arab 1264
 
0.8%
states 1254
 
0.8%
Other values (298) 128733
83.1%
2025-01-05T20:42:05.117239image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 146317
 
13.7%
n 86336
 
8.1%
i 84492
 
7.9%
e 75402
 
7.1%
r 61025
 
5.7%
54847
 
5.1%
o 53473
 
5.0%
t 45105
 
4.2%
l 44827
 
4.2%
s 43108
 
4.0%
Other values (49) 373043
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1067975
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 146317
 
13.7%
n 86336
 
8.1%
i 84492
 
7.9%
e 75402
 
7.1%
r 61025
 
5.7%
54847
 
5.1%
o 53473
 
5.0%
t 45105
 
4.2%
l 44827
 
4.2%
s 43108
 
4.0%
Other values (49) 373043
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1067975
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 146317
 
13.7%
n 86336
 
8.1%
i 84492
 
7.9%
e 75402
 
7.1%
r 61025
 
5.7%
54847
 
5.1%
o 53473
 
5.0%
t 45105
 
4.2%
l 44827
 
4.2%
s 43108
 
4.0%
Other values (49) 373043
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1067975
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 146317
 
13.7%
n 86336
 
8.1%
i 84492
 
7.9%
e 75402
 
7.1%
r 61025
 
5.7%
54847
 
5.1%
o 53473
 
5.0%
t 45105
 
4.2%
l 44827
 
4.2%
s 43108
 
4.0%
Other values (49) 373043
34.9%
Distinct971
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2025-01-05T20:42:05.291836image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.53709
Min length1

Characters and Unicode

Total characters553709
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrespond
2nd rowpopulation
3rd rowhimself
4th rowsafe
5th rowwell
ValueCountFrequency (%)
charge 142
 
0.1%
pull 136
 
0.1%
institution 135
 
0.1%
onto 132
 
0.1%
seem 131
 
0.1%
rule 131
 
0.1%
school 131
 
0.1%
wife 130
 
0.1%
case 130
 
0.1%
child 129
 
0.1%
Other values (961) 98673
98.7%
2025-01-05T20:42:05.530853image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 74072
13.4%
t 44659
 
8.1%
r 42864
 
7.7%
a 42441
 
7.7%
o 39241
 
7.1%
i 38804
 
7.0%
n 36287
 
6.6%
s 31285
 
5.7%
l 27684
 
5.0%
c 23884
 
4.3%
Other values (25) 152488
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 553709
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 74072
13.4%
t 44659
 
8.1%
r 42864
 
7.7%
a 42441
 
7.7%
o 39241
 
7.1%
i 38804
 
7.0%
n 36287
 
6.6%
s 31285
 
5.7%
l 27684
 
5.0%
c 23884
 
4.3%
Other values (25) 152488
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 553709
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 74072
13.4%
t 44659
 
8.1%
r 42864
 
7.7%
a 42441
 
7.7%
o 39241
 
7.1%
i 38804
 
7.0%
n 36287
 
6.6%
s 31285
 
5.7%
l 27684
 
5.0%
c 23884
 
4.3%
Other values (25) 152488
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 553709
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 74072
13.4%
t 44659
 
8.1%
r 42864
 
7.7%
a 42441
 
7.7%
o 39241
 
7.1%
i 38804
 
7.0%
n 36287
 
6.6%
s 31285
 
5.7%
l 27684
 
5.0%
c 23884
 
4.3%
Other values (25) 152488
27.5%

Campaign ID
Text

Missing 

Distinct20132
Distinct (%)100.0%
Missing79868
Missing (%)79.9%
Memory size4.2 MiB
2025-01-05T20:42:05.652742image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters724752
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20132 ?
Unique (%)100.0%

Sample

1st row6052b8d2-3403-4447-b0b4-c5ce20e6c8d0
2nd row2b568bef-54b5-43e2-a5f7-c1ae802b0547
3rd row98705a1e-2573-4cb8-983b-2112d4b140c9
4th row6b15bf9b-14cc-4ab9-9168-56322057b61e
5th rowd69e6171-e44b-4588-b52e-2816900af9bc
ValueCountFrequency (%)
6b15bf9b-14cc-4ab9-9168-56322057b61e 1
 
< 0.1%
ce7c5222-96bf-4428-89b8-c5390bf4e5c2 1
 
< 0.1%
a0edff02-7590-4f5e-b81f-fca7172fcd73 1
 
< 0.1%
32decc94-37c2-47e2-b9d6-ab51d974bb77 1
 
< 0.1%
2b568bef-54b5-43e2-a5f7-c1ae802b0547 1
 
< 0.1%
9ffda691-fc52-47dc-93bf-041eb2eb3974 1
 
< 0.1%
7941b7a5-9607-4c04-b247-0d90adf14b76 1
 
< 0.1%
884f4be9-1a8f-4501-9061-64b7d4d0f1d5 1
 
< 0.1%
15d1e055-d95b-4606-8681-3f223c018940 1
 
< 0.1%
19e199c3-ddbf-4a1b-ac7f-479b4d41beb8 1
 
< 0.1%
Other values (20122) 20122
> 99.9%
2025-01-05T20:42:05.837665image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 80528
 
11.1%
4 58029
 
8.0%
b 42883
 
5.9%
8 42805
 
5.9%
a 42571
 
5.9%
9 42361
 
5.8%
f 38017
 
5.2%
7 37927
 
5.2%
2 37878
 
5.2%
6 37871
 
5.2%
Other values (7) 263882
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 724752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 80528
 
11.1%
4 58029
 
8.0%
b 42883
 
5.9%
8 42805
 
5.9%
a 42571
 
5.9%
9 42361
 
5.8%
f 38017
 
5.2%
7 37927
 
5.2%
2 37878
 
5.2%
6 37871
 
5.2%
Other values (7) 263882
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 724752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 80528
 
11.1%
4 58029
 
8.0%
b 42883
 
5.9%
8 42805
 
5.9%
a 42571
 
5.9%
9 42361
 
5.8%
f 38017
 
5.2%
7 37927
 
5.2%
2 37878
 
5.2%
6 37871
 
5.2%
Other values (7) 263882
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 724752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 80528
 
11.1%
4 58029
 
8.0%
b 42883
 
5.9%
8 42805
 
5.9%
a 42571
 
5.9%
9 42361
 
5.8%
f 38017
 
5.2%
7 37927
 
5.2%
2 37878
 
5.2%
6 37871
 
5.2%
Other values (7) 263882
36.4%

Sentiment
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing50100
Missing (%)50.1%
Memory size6.1 MiB
Positive
16738 
Neutral
16645 
Negative
16517 

Length

Max length8
Median length8
Mean length7.6664329
Min length7

Characters and Unicode

Total characters382555
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNegative
2nd rowNeutral
3rd rowNeutral
4th rowNeutral
5th rowNeutral

Common Values

ValueCountFrequency (%)
Positive 16738
 
16.7%
Neutral 16645
 
16.6%
Negative 16517
 
16.5%
(Missing) 50100
50.1%

Length

2025-01-05T20:42:05.917012image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-05T20:42:05.965056image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
positive 16738
33.5%
neutral 16645
33.4%
negative 16517
33.1%

Most occurring characters

ValueCountFrequency (%)
e 66417
17.4%
i 49993
13.1%
t 49900
13.0%
v 33255
8.7%
N 33162
8.7%
a 33162
8.7%
o 16738
 
4.4%
P 16738
 
4.4%
s 16738
 
4.4%
u 16645
 
4.4%
Other values (3) 49807
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 382555
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 66417
17.4%
i 49993
13.1%
t 49900
13.0%
v 33255
8.7%
N 33162
8.7%
a 33162
8.7%
o 16738
 
4.4%
P 16738
 
4.4%
s 16738
 
4.4%
u 16645
 
4.4%
Other values (3) 49807
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 382555
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 66417
17.4%
i 49993
13.1%
t 49900
13.0%
v 33255
8.7%
N 33162
8.7%
a 33162
8.7%
o 16738
 
4.4%
P 16738
 
4.4%
s 16738
 
4.4%
u 16645
 
4.4%
Other values (3) 49807
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 382555
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 66417
17.4%
i 49993
13.1%
t 49900
13.0%
v 33255
8.7%
N 33162
8.7%
a 33162
8.7%
o 16738
 
4.4%
P 16738
 
4.4%
s 16738
 
4.4%
u 16645
 
4.4%
Other values (3) 49807
13.0%

Influencer ID
Text

Missing 

Distinct9994
Distinct (%)100.0%
Missing90006
Missing (%)90.0%
Memory size3.6 MiB
2025-01-05T20:42:06.061554image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters359784
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9994 ?
Unique (%)100.0%

Sample

1st rowaca49940-8445-483f-9ab8-0db0167bb2db
2nd row50fa2d5d-a67a-486e-b162-8c5f355abe7e
3rd row020d4653-f752-4176-a694-21b10355604e
4th row9d0f660f-c482-417a-ade5-c0abbf1ea859
5th row09fd85ec-fe5b-456a-9f30-807638b5fb4d
ValueCountFrequency (%)
56f0c814-0a28-444e-b93a-85f35a3e6a50 1
 
< 0.1%
67325eaf-81fb-405b-8786-1918430b054d 1
 
< 0.1%
9f33b585-cff9-47c2-8756-37132b964183 1
 
< 0.1%
a25e22a3-07ca-4d00-9fe5-ce49cd491a10 1
 
< 0.1%
2d033a08-e39a-4ff1-a7f3-a1aa7a2afbd8 1
 
< 0.1%
9fc98a20-a909-433d-9675-14414bada580 1
 
< 0.1%
88be36e3-6e1a-4fa1-beea-4c416e603ece 1
 
< 0.1%
de5a9c6f-8b58-4c94-bd6f-c2ba792ea267 1
 
< 0.1%
6d2470d8-3e20-46d5-9403-3d1b03fbd7ad 1
 
< 0.1%
8593cd4c-f983-430b-b611-7f9f7e88f0f5 1
 
< 0.1%
Other values (9984) 9984
99.9%
2025-01-05T20:42:06.246382image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 39976
 
11.1%
4 28612
 
8.0%
8 21237
 
5.9%
9 21236
 
5.9%
a 21158
 
5.9%
b 21012
 
5.8%
2 19023
 
5.3%
6 19018
 
5.3%
0 18975
 
5.3%
5 18903
 
5.3%
Other values (7) 130634
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 359784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 39976
 
11.1%
4 28612
 
8.0%
8 21237
 
5.9%
9 21236
 
5.9%
a 21158
 
5.9%
b 21012
 
5.8%
2 19023
 
5.3%
6 19018
 
5.3%
0 18975
 
5.3%
5 18903
 
5.3%
Other values (7) 130634
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 359784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 39976
 
11.1%
4 28612
 
8.0%
8 21237
 
5.9%
9 21236
 
5.9%
a 21158
 
5.9%
b 21012
 
5.8%
2 19023
 
5.3%
6 19018
 
5.3%
0 18975
 
5.3%
5 18903
 
5.3%
Other values (7) 130634
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 359784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 39976
 
11.1%
4 28612
 
8.0%
8 21237
 
5.9%
9 21236
 
5.9%
a 21158
 
5.9%
b 21012
 
5.8%
2 19023
 
5.3%
6 19018
 
5.3%
0 18975
 
5.3%
5 18903
 
5.3%
Other values (7) 130634
36.3%

Interactions

2025-01-05T20:42:01.088272image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.148169image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.630522image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.100968image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.588280image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.128308image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.604525image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:01.154602image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.215461image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.695093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.174665image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.652209image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.194831image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.665350image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:01.220621image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.279467image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.748154image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.237855image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.721690image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.263903image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.739432image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:01.292306image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.359456image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.832800image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.312019image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.783975image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.336334image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.817662image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:01.358056image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.425602image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.898638image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.377728image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.856408image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.401716image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.885591image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:01.422187image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.488190image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.969287image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.449857image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.984534image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.471767image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.953239image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:01.491939image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:58.566275image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.040001image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:41:59.519096image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.067870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:00.535034image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-01-05T20:42:01.021536image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-01-05T20:42:06.326714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Audience AgeAudience GenderCommentsEngagement RateImpressionsLikesPlatformPost TypeReachSentimentShares
Audience Age1.0000.0030.0060.0030.0010.0040.0000.0040.0040.0000.003
Audience Gender0.0031.0000.0000.0000.0050.0000.0000.0000.0000.0000.003
Comments0.0060.0001.0000.263-0.001-0.0010.0000.000-0.0020.0100.001
Engagement Rate0.0030.0000.2631.0000.0020.5210.0000.000-0.7650.0000.107
Impressions0.0010.005-0.0010.0021.000-0.0010.0000.000-0.0020.0000.001
Likes0.0040.000-0.0010.521-0.0011.0000.0010.0040.0020.0060.002
Platform0.0000.0000.0000.0000.0000.0011.0000.0030.0020.0070.005
Post Type0.0040.0000.0000.0000.0000.0040.0031.0000.0000.0000.002
Reach0.0040.000-0.002-0.765-0.0020.0020.0020.0001.0000.005-0.002
Sentiment0.0000.0000.0100.0000.0000.0060.0070.0000.0051.0000.001
Shares0.0030.0030.0010.1070.0010.0020.0050.002-0.0020.0011.000

Missing values

2025-01-05T20:42:01.606687image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-05T20:42:01.828847image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-05T20:42:02.066693image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PlatformPost IDPost TypePost ContentPost TimestampLikesCommentsSharesImpressionsReachEngagement RateAudience AgeAudience GenderAudience LocationAudience InterestsCampaign IDSentimentInfluencer ID
0LinkedInd49dadb4-fc1e-4775-88ad-d60b75cf5123VideoWant that according same wish.2021-04-23 08:19:49.25510841577852619184231.3229OtherUnited KingdomrespondNaNNegativeNaN
1Instagram16ce29c5-2dc7-478f-9814-a86754f3ce8bVideoIncrease fast partner south.2023-07-30 06:40:06.356134908125851223388428.7848MaleGreecepopulationNaNNeutralNaN
2Instagram6a6cc741-72f4-4111-95a0-c5ee04a175a8VideoLawyer behavior born window couple sister.2021-12-05 03:56:53.211700412419582165849104.7155MaleUnited States Virgin IslandshimselfNaNNeutralNaN
3Twitter0b5a3e70-c556-43cb-ad4f-a1d0003348bcImageStreet great decade must.2021-09-28 20:39:00.227679667153297191351924.1354OtherZambiasafeNaNNaNNaN
4Facebooka7d3e958-e2fe-411e-a24a-b96e821fc050LinkAt clear meeting with reason ground continue contain.2023-02-27 17:51:21.425074659215343312203244.6933OtherAfghanistanwell6052b8d2-3403-4447-b0b4-c5ce20e6c8d0NaNNaN
5Facebook0922ea18-ea90-4302-b126-bd95f85a0926VideoVote American state change simply cup.2021-11-23 08:32:30.0811955104381103871266739.6759FemaleDjiboutishe2b568bef-54b5-43e2-a5f7-c1ae802b0547NeutralNaN
6Instagram0510219c-c413-4203-a9c6-e176a299ce10LinkCommunity seek Republican cold though ability understand event.2022-09-28 06:32:05.116755874239915681155377.5341OtherJamaicafamilyNaNNeutralNaN
7LinkedInc394636e-4e5c-4453-b096-597e530bd4ebLinkWin guess me report true.2023-07-27 06:32:01.557935627135909436149856.8863OtherGibraltarkeepNaNNeutralNaN
8LinkedIn75142995-8ed2-419d-9632-72a0b631175bLinkAction other would side.2023-01-04 16:22:21.0764161000378948539369539.8423MaleEstonianumberNaNNaNNaN
9Twitteraeaed62e-bfa7-4f81-aea7-3ce2413ac473VideoUsually personal small create.2021-07-18 07:55:37.14011857313586769250828.5546OtherGuatemalamainNaNPositiveNaN
PlatformPost IDPost TypePost ContentPost TimestampLikesCommentsSharesImpressionsReachEngagement RateAudience AgeAudience GenderAudience LocationAudience InterestsCampaign IDSentimentInfluencer ID
99990LinkedInd633444f-9f4a-41e9-89b6-a0207c23da1bLinkCould little close while quite experience heart.2021-12-09 20:55:28.874426422211865419466715.4153MaleVenezuelamanage7941b7a5-9607-4c04-b247-0d90adf14b76NegativeNaN
99991LinkedIn2862509e-a3f1-4845-8735-a1b939d224cfLinkDecision stand upon election but color task.2022-01-08 13:39:18.849576624258276559356625.4931FemaleCanadaforceNaNNaNNaN
99992LinkedIn3f4d6bd5-c29a-4a8a-a0ef-7f34b9851acbImageRecently lose candidate eat already.2023-04-05 00:15:59.2905893792371794315160649.5056MaleSeychellesgasNaNPositiveNaN
99993Instagramffbccca5-680a-4a10-a08f-0369c563399dImageThree few major chance leader.2022-01-29 04:20:02.860819552941707076491210.5758MaleTanzaniacharacterNaNNeutralbc53de83-321d-4569-8c85-fe7190c9bd32
99994Facebookbfc611aa-71e3-462d-a200-c671b5f2dd45VideoStage can finish high attack practice.2024-02-25 16:53:25.80444284934323784603151.7461OtherCanadaeveningNaNNaN8593cd4c-f983-430b-b611-7f9f7e88f0f5
99995LinkedIn26534a4f-0cb6-4bb3-b852-d1cadc15584eLinkTrial serious region bit.2021-12-14 08:14:35.616751555491154685237744.6462MaleBrunei DarussalamdayNaNPositiveNaN
99996Facebookdf9577d4-9338-4f41-99f9-8e8f1e34febdLinkPretty general by scene risk.2022-06-24 00:28:30.81779134176764448260518.9335MaleHondurasoverNaNNaNNaN
99997LinkedIn43cd806e-704b-4718-9924-2555c189f8b2LinkDevelop school account wish rate.2023-12-21 23:58:07.906688442155758746461614.5638MaleVanuatufloor9ffda691-fc52-47dc-93bf-041eb2eb3974NeutralNaN
99998Instagram3485970a-0f34-4304-a60a-f4e3de8e1917LinkJust might language idea answer data idea.2024-03-01 21:03:06.6676233134171405487608143.0939FemaleSaint BarthelemykeyNaNNaNNaN
99999Twitter6b1406b0-adca-43e5-abde-799746a9b50aLinkCold money inside deep.2022-04-01 23:34:15.201011271381482179223014.0422FemaleUnited States Virgin Islandsrange3302de60-24c2-4995-9c24-6dcaee99071aNaNNaN